To support GPU programming, the NVPTX back-end supports a subset of LLVM IR along with a defined set of conventions used to represent GPU programming concepts. This document provides an overview of the general usage of the back- end, including a description of the conventions used and the set of accepted LLVM IR.
Note
This document assumes a basic familiarity with CUDA and the PTX assembly language. Information about the CUDA Driver API and the PTX assembly language can be found in the CUDA documentation.
In PTX, there are two types of functions: device functions, which are only
callable by device code, and kernel functions, which are callable by host
code. By default, the back-end will emit device functions. Metadata is used to
declare a function as a kernel function. This metadata is attached to the
nvvm.annotations named metadata object, and has the following format:
!0 = !{<function-ref>, metadata !"kernel", i32 1}
The first parameter is a reference to the kernel function. The following
example shows a kernel function calling a device function in LLVM IR. The
function @my_kernel is callable from host code, but @my_fmad is not.
define float @my_fmad(float %x, float %y, float %z) {
%mul = fmul float %x, %y
%add = fadd float %mul, %z
ret float %add
}
define void @my_kernel(float* %ptr) {
%val = load float, float* %ptr
%ret = call float @my_fmad(float %val, float %val, float %val)
store float %ret, float* %ptr
ret void
}
!nvvm.annotations = !{!1}
!1 = !{void (float*)* @my_kernel, !"kernel", i32 1}When compiled, the PTX kernel functions are callable by host-side code.
The NVPTX back-end uses the following address space mapping:
Address Space Memory Space 0 Generic 1 Global 2 Internal Use 3 Shared 4 Constant 5 Local
Every global variable and pointer type is assigned to one of these address spaces, with 0 being the default address space. Intrinsics are provided which can be used to convert pointers between the generic and non-generic address spaces.
As an example, the following IR will define an array @g that resides in
global device memory.
@g = internal addrspace(1) global [4 x i32] [ i32 0, i32 1, i32 2, i32 3 ]LLVM IR functions can read and write to this array, and host-side code can copy data to it by name with the CUDA Driver API.
Note that since address space 0 is the generic space, it is illegal to have
global variables in address space 0. Address space 0 is the default address
space in LLVM, so the addrspace(N) annotation is required for global
variables.
The NVPTX target uses the module triple to select between 32/64-bit code
generation and the driver-compiler interface to use. The triple architecture
can be one of nvptx (32-bit PTX) or nvptx64 (64-bit PTX). The
operating system should be one of cuda or nvcl, which determines the
interface used by the generated code to communicate with the driver. Most
users will want to use cuda as the operating system, which makes the
generated PTX compatible with the CUDA Driver API.
Example: 32-bit PTX for CUDA Driver API: nvptx-nvidia-cuda
Example: 64-bit PTX for CUDA Driver API: nvptx64-nvidia-cuda
These are overloaded intrinsics. You can use these on any pointer types.
declare i8* @llvm.nvvm.ptr.global.to.gen.p0i8.p1i8(i8 addrspace(1)*)
declare i8* @llvm.nvvm.ptr.shared.to.gen.p0i8.p3i8(i8 addrspace(3)*)
declare i8* @llvm.nvvm.ptr.constant.to.gen.p0i8.p4i8(i8 addrspace(4)*)
declare i8* @llvm.nvvm.ptr.local.to.gen.p0i8.p5i8(i8 addrspace(5)*)The 'llvm.nvvm.ptr.*.to.gen' intrinsics convert a pointer in a non-generic
address space to a generic address space pointer.
These intrinsics modify the pointer value to be a valid generic address space pointer.
These are overloaded intrinsics. You can use these on any pointer types.
declare i8 addrspace(1)* @llvm.nvvm.ptr.gen.to.global.p1i8.p0i8(i8*)
declare i8 addrspace(3)* @llvm.nvvm.ptr.gen.to.shared.p3i8.p0i8(i8*)
declare i8 addrspace(4)* @llvm.nvvm.ptr.gen.to.constant.p4i8.p0i8(i8*)
declare i8 addrspace(5)* @llvm.nvvm.ptr.gen.to.local.p5i8.p0i8(i8*)The 'llvm.nvvm.ptr.gen.to.*' intrinsics convert a pointer in the generic
address space to a pointer in the target address space. Note that these
intrinsics are only useful if the address space of the target address space of
the pointer is known. It is not legal to use address space conversion
intrinsics to convert a pointer from one non-generic address space to another
non-generic address space.
These intrinsics modify the pointer value to be a valid pointer in the target non-generic address space.
declare i32 @llvm.nvvm.read.ptx.sreg.tid.x()
declare i32 @llvm.nvvm.read.ptx.sreg.tid.y()
declare i32 @llvm.nvvm.read.ptx.sreg.tid.z()
declare i32 @llvm.nvvm.read.ptx.sreg.ntid.x()
declare i32 @llvm.nvvm.read.ptx.sreg.ntid.y()
declare i32 @llvm.nvvm.read.ptx.sreg.ntid.z()
declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.x()
declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.y()
declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.z()
declare i32 @llvm.nvvm.read.ptx.sreg.nctaid.x()
declare i32 @llvm.nvvm.read.ptx.sreg.nctaid.y()
declare i32 @llvm.nvvm.read.ptx.sreg.nctaid.z()
declare i32 @llvm.nvvm.read.ptx.sreg.warpsize()The '@llvm.nvvm.read.ptx.sreg.*' intrinsics provide access to the PTX
special registers, in particular the kernel launch bounds. These registers
map in the following way to CUDA builtins:
CUDA Builtin PTX Special Register Intrinsic threadId@llvm.nvvm.read.ptx.sreg.tid.*blockIdx@llvm.nvvm.read.ptx.sreg.ctaid.*blockDim@llvm.nvvm.read.ptx.sreg.ntid.*gridDim@llvm.nvvm.read.ptx.sreg.nctaid.*
declare void @llvm.nvvm.barrier0()The '@llvm.nvvm.barrier0()' intrinsic emits a PTX bar.sync 0
instruction, equivalent to the __syncthreads() call in CUDA.
For the full set of NVPTX intrinsics, please see the
include/llvm/IR/IntrinsicsNVVM.td file in the LLVM source tree.
The CUDA Toolkit comes with an LLVM bitcode library called libdevice that
implements many common mathematical functions. This library can be used as a
high-performance math library for any compilers using the LLVM NVPTX target.
The library can be found under nvvm/libdevice/ in the CUDA Toolkit and
there is a separate version for each compute architecture.
For a list of all math functions implemented in libdevice, see libdevice Users Guide.
To accommodate various math-related compiler flags that can affect code
generation of libdevice code, the library code depends on a special LLVM IR
pass (NVVMReflect) to handle conditional compilation within LLVM IR. This
pass looks for calls to the @__nvvm_reflect function and replaces them
with constants based on the defined reflection parameters. Such conditional
code often follows a pattern:
float my_function(float a) {
if (__nvvm_reflect("FASTMATH"))
return my_function_fast(a);
else
return my_function_precise(a);
}The default value for all unspecified reflection parameters is zero.
The NVVMReflect pass should be executed early in the optimization
pipeline, immediately after the link stage. The internalize pass is also
recommended to remove unused math functions from the resulting PTX. For an
input IR module module.bc, the following compilation flow is recommended:
- Save list of external functions in
module.bc - Link
module.bcwithlibdevice.compute_XX.YY.bc - Internalize all functions not in list from (1)
- Eliminate all unused internal functions
- Run
NVVMReflectpass - Run standard optimization pipeline
Note
linkonce and linkonce_odr linkage types are not suitable for the
libdevice functions. It is possible to link two IR modules that have been
linked against libdevice using different reflection variables.
Since the NVVMReflect pass replaces conditionals with constants, it will
often leave behind dead code of the form:
entry:
..
br i1 true, label %foo, label %bar
foo:
..
bar:
; Dead code
..Therefore, it is recommended that NVVMReflect is executed early in the
optimization pipeline before dead-code elimination.
The libdevice library currently uses the following reflection parameters to control code generation:
| Flag | Description |
|---|---|
__CUDA_FTZ=[0,1] |
Use optimized code paths that flush subnormals to zero |
To ensure that all dead code caused by the reflection pass is eliminated, it
is recommended that the reflection pass is executed early in the LLVM IR
optimization pipeline. The pass takes an optional mapping of reflection
parameter name to an integer value. This mapping can be specified as either a
command-line option to opt or as an LLVM StringMap<int> object when
programmatically creating a pass pipeline.
With opt:
# opt -nvvm-reflect -nvvm-reflect-list=<var>=<value>,<var>=<value> module.bc -o module.reflect.bc
With programmatic pass pipeline:
extern FunctionPass *llvm::createNVVMReflectPass(const StringMap<int>& Mapping);
StringMap<int> ReflectParams;
ReflectParams["__CUDA_FTZ"] = 1;
Passes.add(createNVVMReflectPass(ReflectParams));The most common way to execute PTX assembly on a GPU device is to use the CUDA Driver API. This API is a low-level interface to the GPU driver and allows for JIT compilation of PTX code to native GPU machine code.
Initializing the Driver API:
CUdevice device;
CUcontext context;
// Initialize the driver API
cuInit(0);
// Get a handle to the first compute device
cuDeviceGet(&device, 0);
// Create a compute device context
cuCtxCreate(&context, 0, device);JIT compiling a PTX string to a device binary:
CUmodule module;
CUfunction function;
// JIT compile a null-terminated PTX string
cuModuleLoadData(&module, (void*)PTXString);
// Get a handle to the "myfunction" kernel function
cuModuleGetFunction(&function, module, "myfunction");For full examples of executing PTX assembly, please see the CUDA Samples distribution.
When linking with libdevice, the NVVMReflect pass must be used. See
:ref:`libdevice` for more information.
To start, let us take a look at a simple compute kernel written directly in LLVM IR. The kernel implements vector addition, where each thread computes one element of the output vector C from the input vectors A and B. To make this easier, we also assume that only a single CTA (thread block) will be launched, and that it will be one dimensional.
target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64"
target triple = "nvptx64-nvidia-cuda"
; Intrinsic to read X component of thread ID
declare i32 @llvm.nvvm.read.ptx.sreg.tid.x() readnone nounwind
define void @kernel(float addrspace(1)* %A,
float addrspace(1)* %B,
float addrspace(1)* %C) {
entry:
; What is my ID?
%id = tail call i32 @llvm.nvvm.read.ptx.sreg.tid.x() readnone nounwind
; Compute pointers into A, B, and C
%ptrA = getelementptr float, float addrspace(1)* %A, i32 %id
%ptrB = getelementptr float, float addrspace(1)* %B, i32 %id
%ptrC = getelementptr float, float addrspace(1)* %C, i32 %id
; Read A, B
%valA = load float, float addrspace(1)* %ptrA, align 4
%valB = load float, float addrspace(1)* %ptrB, align 4
; Compute C = A + B
%valC = fadd float %valA, %valB
; Store back to C
store float %valC, float addrspace(1)* %ptrC, align 4
ret void
}
!nvvm.annotations = !{!0}
!0 = !{void (float addrspace(1)*,
float addrspace(1)*,
float addrspace(1)*)* @kernel, !"kernel", i32 1}We can use the LLVM llc tool to directly run the NVPTX code generator:
# llc -mcpu=sm_20 kernel.ll -o kernel.ptx
Note
If you want to generate 32-bit code, change p:64:64:64 to p:32:32:32
in the module data layout string and use nvptx-nvidia-cuda as the
target triple.
The output we get from llc (as of LLVM 3.4):
//
// Generated by LLVM NVPTX Back-End
//
.version 3.1
.target sm_20
.address_size 64
// .globl kernel
// @kernel
.visible .entry kernel(
.param .u64 kernel_param_0,
.param .u64 kernel_param_1,
.param .u64 kernel_param_2
)
{
.reg .f32 %f<4>;
.reg .s32 %r<2>;
.reg .s64 %rl<8>;
// BB#0: // %entry
ld.param.u64 %rl1, [kernel_param_0];
mov.u32 %r1, %tid.x;
mul.wide.s32 %rl2, %r1, 4;
add.s64 %rl3, %rl1, %rl2;
ld.param.u64 %rl4, [kernel_param_1];
add.s64 %rl5, %rl4, %rl2;
ld.param.u64 %rl6, [kernel_param_2];
add.s64 %rl7, %rl6, %rl2;
ld.global.f32 %f1, [%rl3];
ld.global.f32 %f2, [%rl5];
add.f32 %f3, %f1, %f2;
st.global.f32 [%rl7], %f3;
ret;
}
Now let us dissect the LLVM IR that makes up this kernel.
The data layout string determines the size in bits of common data types, their ABI alignment, and their storage size. For NVPTX, you should use one of the following:
32-bit PTX:
target datalayout = "e-p:32:32:32-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64"64-bit PTX:
target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64"In this example, we use the @llvm.nvvm.read.ptx.sreg.tid.x intrinsic to
read the X component of the current thread's ID, which corresponds to a read
of register %tid.x in PTX. The NVPTX back-end supports a large set of
intrinsics. A short list is shown below; please see
include/llvm/IR/IntrinsicsNVVM.td for the full list.
| Intrinsic | CUDA Equivalent |
|---|---|
i32 @llvm.nvvm.read.ptx.sreg.tid.{x,y,z} |
threadIdx.{x,y,z} |
i32 @llvm.nvvm.read.ptx.sreg.ctaid.{x,y,z} |
blockIdx.{x,y,z} |
i32 @llvm.nvvm.read.ptx.sreg.ntid.{x,y,z} |
blockDim.{x,y,z} |
i32 @llvm.nvvm.read.ptx.sreg.nctaid.{x,y,z} |
gridDim.{x,y,z} |
void @llvm.nvvm.barrier0() |
__syncthreads() |
You may have noticed that all of the pointer types in the LLVM IR example had an explicit address space specifier. What is address space 1? NVIDIA GPU devices (generally) have four types of memory:
- Global: Large, off-chip memory
- Shared: Small, on-chip memory shared among all threads in a CTA
- Local: Per-thread, private memory
- Constant: Read-only memory shared across all threads
These different types of memory are represented in LLVM IR as address spaces. There is also a fifth address space used by the NVPTX code generator that corresponds to the "generic" address space. This address space can represent addresses in any other address space (with a few exceptions). This allows users to write IR functions that can load/store memory using the same instructions. Intrinsics are provided to convert pointers between the generic and non-generic address spaces.
See :ref:`address_spaces` and :ref:`nvptx_intrinsics` for more information.
In PTX, a function can be either a kernel function (callable from the host
program), or a device function (callable only from GPU code). You can think
of kernel functions as entry-points in the GPU program. To mark an LLVM IR
function as a kernel function, we make use of special LLVM metadata. The
NVPTX back-end will look for a named metadata node called
nvvm.annotations. This named metadata must contain a list of metadata that
describe the IR. For our purposes, we need to declare a metadata node that
assigns the "kernel" attribute to the LLVM IR function that should be emitted
as a PTX kernel function. These metadata nodes take the form:
!{<function ref>, metadata !"kernel", i32 1}
For the previous example, we have:
!nvvm.annotations = !{!0}
!0 = !{void (float addrspace(1)*,
float addrspace(1)*,
float addrspace(1)*)* @kernel, !"kernel", i32 1}Here, we have a single metadata declaration in nvvm.annotations. This
metadata annotates our @kernel function with the kernel attribute.
Generating PTX from LLVM IR is all well and good, but how do we execute it on a real GPU device? The CUDA Driver API provides a convenient mechanism for loading and JIT compiling PTX to a native GPU device, and launching a kernel. The API is similar to OpenCL. A simple example showing how to load and execute our vector addition code is shown below. Note that for brevity this code does not perform much error checking!
Note
You can also use the ptxas tool provided by the CUDA Toolkit to offline
compile PTX to machine code (SASS) for a specific GPU architecture. Such
binaries can be loaded by the CUDA Driver API in the same way as PTX. This
can be useful for reducing startup time by precompiling the PTX kernels.
#include <iostream>
#include <fstream>
#include <cassert>
#include "cuda.h"
void checkCudaErrors(CUresult err) {
assert(err == CUDA_SUCCESS);
}
/// main - Program entry point
int main(int argc, char **argv) {
CUdevice device;
CUmodule cudaModule;
CUcontext context;
CUfunction function;
CUlinkState linker;
int devCount;
// CUDA initialization
checkCudaErrors(cuInit(0));
checkCudaErrors(cuDeviceGetCount(&devCount));
checkCudaErrors(cuDeviceGet(&device, 0));
char name[128];
checkCudaErrors(cuDeviceGetName(name, 128, device));
std::cout << "Using CUDA Device [0]: " << name << "\n";
int devMajor, devMinor;
checkCudaErrors(cuDeviceComputeCapability(&devMajor, &devMinor, device));
std::cout << "Device Compute Capability: "
<< devMajor << "." << devMinor << "\n";
if (devMajor < 2) {
std::cerr << "ERROR: Device 0 is not SM 2.0 or greater\n";
return 1;
}
std::ifstream t("kernel.ptx");
if (!t.is_open()) {
std::cerr << "kernel.ptx not found\n";
return 1;
}
std::string str((std::istreambuf_iterator<char>(t)),
std::istreambuf_iterator<char>());
// Create driver context
checkCudaErrors(cuCtxCreate(&context, 0, device));
// Create module for object
checkCudaErrors(cuModuleLoadDataEx(&cudaModule, str.c_str(), 0, 0, 0));
// Get kernel function
checkCudaErrors(cuModuleGetFunction(&function, cudaModule, "kernel"));
// Device data
CUdeviceptr devBufferA;
CUdeviceptr devBufferB;
CUdeviceptr devBufferC;
checkCudaErrors(cuMemAlloc(&devBufferA, sizeof(float)*16));
checkCudaErrors(cuMemAlloc(&devBufferB, sizeof(float)*16));
checkCudaErrors(cuMemAlloc(&devBufferC, sizeof(float)*16));
float* hostA = new float[16];
float* hostB = new float[16];
float* hostC = new float[16];
// Populate input
for (unsigned i = 0; i != 16; ++i) {
hostA[i] = (float)i;
hostB[i] = (float)(2*i);
hostC[i] = 0.0f;
}
checkCudaErrors(cuMemcpyHtoD(devBufferA, &hostA[0], sizeof(float)*16));
checkCudaErrors(cuMemcpyHtoD(devBufferB, &hostB[0], sizeof(float)*16));
unsigned blockSizeX = 16;
unsigned blockSizeY = 1;
unsigned blockSizeZ = 1;
unsigned gridSizeX = 1;
unsigned gridSizeY = 1;
unsigned gridSizeZ = 1;
// Kernel parameters
void *KernelParams[] = { &devBufferA, &devBufferB, &devBufferC };
std::cout << "Launching kernel\n";
// Kernel launch
checkCudaErrors(cuLaunchKernel(function, gridSizeX, gridSizeY, gridSizeZ,
blockSizeX, blockSizeY, blockSizeZ,
0, NULL, KernelParams, NULL));
// Retrieve device data
checkCudaErrors(cuMemcpyDtoH(&hostC[0], devBufferC, sizeof(float)*16));
std::cout << "Results:\n";
for (unsigned i = 0; i != 16; ++i) {
std::cout << hostA[i] << " + " << hostB[i] << " = " << hostC[i] << "\n";
}
// Clean up after ourselves
delete [] hostA;
delete [] hostB;
delete [] hostC;
// Clean-up
checkCudaErrors(cuMemFree(devBufferA));
checkCudaErrors(cuMemFree(devBufferB));
checkCudaErrors(cuMemFree(devBufferC));
checkCudaErrors(cuModuleUnload(cudaModule));
checkCudaErrors(cuCtxDestroy(context));
return 0;
}You will need to link with the CUDA driver and specify the path to cuda.h.
# clang++ sample.cpp -o sample -O2 -g -I/usr/local/cuda-5.5/include -lcuda
We don't need to specify a path to libcuda.so since this is installed in a
system location by the driver, not the CUDA toolkit.
If everything goes as planned, you should see the following output when running the compiled program:
Using CUDA Device [0]: GeForce GTX 680
Device Compute Capability: 3.0
Launching kernel
Results:
0 + 0 = 0
1 + 2 = 3
2 + 4 = 6
3 + 6 = 9
4 + 8 = 12
5 + 10 = 15
6 + 12 = 18
7 + 14 = 21
8 + 16 = 24
9 + 18 = 27
10 + 20 = 30
11 + 22 = 33
12 + 24 = 36
13 + 26 = 39
14 + 28 = 42
15 + 30 = 45
Note
You will likely see a different device identifier based on your hardware
In this tutorial, we show a simple example of linking LLVM IR with the
libdevice library. We will use the same kernel as the previous tutorial,
except that we will compute C = pow(A, B) instead of C = A + B.
Libdevice provides an __nv_powf function that we will use.
target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:64"
target triple = "nvptx64-nvidia-cuda"
; Intrinsic to read X component of thread ID
declare i32 @llvm.nvvm.read.ptx.sreg.tid.x() readnone nounwind
; libdevice function
declare float @__nv_powf(float, float)
define void @kernel(float addrspace(1)* %A,
float addrspace(1)* %B,
float addrspace(1)* %C) {
entry:
; What is my ID?
%id = tail call i32 @llvm.nvvm.read.ptx.sreg.tid.x() readnone nounwind
; Compute pointers into A, B, and C
%ptrA = getelementptr float, float addrspace(1)* %A, i32 %id
%ptrB = getelementptr float, float addrspace(1)* %B, i32 %id
%ptrC = getelementptr float, float addrspace(1)* %C, i32 %id
; Read A, B
%valA = load float, float addrspace(1)* %ptrA, align 4
%valB = load float, float addrspace(1)* %ptrB, align 4
; Compute C = pow(A, B)
%valC = call float @__nv_powf(float %valA, float %valB)
; Store back to C
store float %valC, float addrspace(1)* %ptrC, align 4
ret void
}
!nvvm.annotations = !{!0}
!0 = !{void (float addrspace(1)*,
float addrspace(1)*,
float addrspace(1)*)* @kernel, !"kernel", i32 1}To compile this kernel, we perform the following steps:
- Link with libdevice
- Internalize all but the public kernel function
- Run
NVVMReflectand set__CUDA_FTZto 0 - Optimize the linked module
- Codegen the module
These steps can be performed by the LLVM llvm-link, opt, and llc
tools. In a complete compiler, these steps can also be performed entirely
programmatically by setting up an appropriate pass configuration (see
:ref:`libdevice`).
# llvm-link t2.bc libdevice.compute_20.10.bc -o t2.linked.bc
# opt -internalize -internalize-public-api-list=kernel -nvvm-reflect-list=__CUDA_FTZ=0 -nvvm-reflect -O3 t2.linked.bc -o t2.opt.bc
# llc -mcpu=sm_20 t2.opt.bc -o t2.ptx
Note
The -nvvm-reflect-list=_CUDA_FTZ=0 is not strictly required, as any
undefined variables will default to zero. It is shown here for evaluation
purposes.
This gives us the following PTX (excerpt):
//
// Generated by LLVM NVPTX Back-End
//
.version 3.1
.target sm_20
.address_size 64
// .globl kernel
// @kernel
.visible .entry kernel(
.param .u64 kernel_param_0,
.param .u64 kernel_param_1,
.param .u64 kernel_param_2
)
{
.reg .pred %p<30>;
.reg .f32 %f<111>;
.reg .s32 %r<21>;
.reg .s64 %rl<8>;
// BB#0: // %entry
ld.param.u64 %rl2, [kernel_param_0];
mov.u32 %r3, %tid.x;
ld.param.u64 %rl3, [kernel_param_1];
mul.wide.s32 %rl4, %r3, 4;
add.s64 %rl5, %rl2, %rl4;
ld.param.u64 %rl6, [kernel_param_2];
add.s64 %rl7, %rl3, %rl4;
add.s64 %rl1, %rl6, %rl4;
ld.global.f32 %f1, [%rl5];
ld.global.f32 %f2, [%rl7];
setp.eq.f32 %p1, %f1, 0f3F800000;
setp.eq.f32 %p2, %f2, 0f00000000;
or.pred %p3, %p1, %p2;
@%p3 bra BB0_1;
bra.uni BB0_2;
BB0_1:
mov.f32 %f110, 0f3F800000;
st.global.f32 [%rl1], %f110;
ret;
BB0_2: // %__nv_isnanf.exit.i
abs.f32 %f4, %f1;
setp.gtu.f32 %p4, %f4, 0f7F800000;
@%p4 bra BB0_4;
// BB#3: // %__nv_isnanf.exit5.i
abs.f32 %f5, %f2;
setp.le.f32 %p5, %f5, 0f7F800000;
@%p5 bra BB0_5;
BB0_4: // %.critedge1.i
add.f32 %f110, %f1, %f2;
st.global.f32 [%rl1], %f110;
ret;
BB0_5: // %__nv_isinff.exit.i
...
BB0_26: // %__nv_truncf.exit.i.i.i.i.i
mul.f32 %f90, %f107, 0f3FB8AA3B;
cvt.rzi.f32.f32 %f91, %f90;
mov.f32 %f92, 0fBF317200;
fma.rn.f32 %f93, %f91, %f92, %f107;
mov.f32 %f94, 0fB5BFBE8E;
fma.rn.f32 %f95, %f91, %f94, %f93;
mul.f32 %f89, %f95, 0f3FB8AA3B;
// inline asm
ex2.approx.ftz.f32 %f88,%f89;
// inline asm
add.f32 %f96, %f91, 0f00000000;
ex2.approx.f32 %f97, %f96;
mul.f32 %f98, %f88, %f97;
setp.lt.f32 %p15, %f107, 0fC2D20000;
selp.f32 %f99, 0f00000000, %f98, %p15;
setp.gt.f32 %p16, %f107, 0f42D20000;
selp.f32 %f110, 0f7F800000, %f99, %p16;
setp.eq.f32 %p17, %f110, 0f7F800000;
@%p17 bra BB0_28;
// BB#27:
fma.rn.f32 %f110, %f110, %f108, %f110;
BB0_28: // %__internal_accurate_powf.exit.i
setp.lt.f32 %p18, %f1, 0f00000000;
setp.eq.f32 %p19, %f3, 0f3F800000;
and.pred %p20, %p18, %p19;
@!%p20 bra BB0_30;
bra.uni BB0_29;
BB0_29:
mov.b32 %r9, %f110;
xor.b32 %r10, %r9, -2147483648;
mov.b32 %f110, %r10;
BB0_30: // %__nv_powf.exit
st.global.f32 [%rl1], %f110;
ret;
}